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ieee International Conference on bioinformatics and Biomedicine (BIBM)

作者： Qiang Zhu Qinghui Dai Bangchao Wang Jinxing Liang Junping Liu Li Li Xinrong Hu Hubei Provincial Engineering Research Center for Intelligent Textile and Fashion Wuha China Engineering Research Center of Hubei Province for Clothing Information Wuhan China School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China

ISBN: (纸本)9781665429825

The approaches based on laboratory experiments to explore the interactions between viruses and their hosts are costly and time-consuming. Due to advances in high-throughput technologies, recent computational methods to predict virus-host interaction have attracted increasing attention. But these methods couldn’t effectively utilize the heterogeneous information of the virus-host interaction network. In this paper, we propose a computational method to predict potential virus-host interaction via network fusion and graph mining, named VHINFGM. Different from existing methods, VHINFGM constructs two different heterogeneous networks from the existing interaction network through similarity network fusion and graph embedding technique. Then, VHINFGM introduces two kinds of meta-path scores to extract features from each heterogeneous graph. Based on this graph mining approach, a mixed feature vector for two heterogeneous networks can be obtained, which can be used as the input of a classifier to predict potential interactions. VHINFGM is verified on four datasets, it can be found that VHINFGM outperforms the state-of-the-art methods. 5 out of the top 10 virus-host interaction reported by VHINFGM has been validated in the biological experiments. Besides, VHINFGM predicts 5 new virus-host relationships, which could guide further research.

关键词： Fuses Conferences Feature extraction Heterogeneous networks biology Data mining bioinformatics

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Transforming UTE-mDixon MR Abdomen-Pelvis Images Into CT by Jointly Leveraging Prior Knowledge and Partial Supervision

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第1期18卷 70-82页

作者： Qian, Pengjiang Zheng, Jiamin Zheng, Qiankun Liu, Yuan Wang, Tingyu Al Helo, Rose Baydoun, Atallah Avril, Norbert Ellis, Rodney J. Friel, Harry Traughber, Melanie S. Devaraj, Ajit Traughber, Bryan Muzic, Raymond F., Jr. Jiangnan Univ Sch Artificial Intelligence & Comp Sci 1800 Lihu Ave Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Jiangsu Key Lab Media Design & Software Technol 1800 Lihu Ave Wuxi 214122 Jiangsu Peoples R China Case Western Reserve Univ Dept Radiol Cleveland OH 44106 USA Case Western Reserve Univ Univ Hosp Case Ctr Imaging Res Cleveland OH 44106 USA Case Western Reserve Univ Dept Biomed Engn Cleveland OH 44106 USA Louis Stokes Cleveland VA Med Ctr Dept Internal Med Cleveland OH 44106 USA Univ Illinois Dept Elect & Comp Engn Champaign IL 61820 USA Univ Hosp Cleveland Dept Radiat Oncol Med Ctr Cleveland OH 44106 USA Philips Healthcare Cleveland OH 44143 USA Philips Res North Amer Cambridge MA 02141 USA Case Western Reserve Univ Case Ctr Imaging Res Cleveland OH 44106 USA Case Western Reserve Univ Dept Radiat Oncol Cleveland OH 44106 USA Univ Hosp Dept Radiat Oncol Seidman Canc Ctr Cleveland OH 44106 USA Louis Stokes Cleveland VA Med Ctr Dept Radiat Oncol Cleveland OH 44106 USA

Computed tomography (CT) provides information for diagnosis, PET attenuation correction (AC), and radiation treatment planning (RTP). Disadvantages of CT include poor soft tissue contrast and exposure to ionizing radiation. While MRI can overcome these disadvantages, it lacks the photon absorption information needed for PET AC and RTP. Thus, an intelligent transformation from MR to CT, i.e., the MR-based synthetic CT generation, is of great interest as it would support PET/MR AC and MR-only RTP. Using an MR pulse sequence that combines ultra-short echo time (UTE) and modified Dixon (mDixon), we propose a novel method for synthetic CT generation jointly leveraging prior knowledge as well as partial supervision (SCT-PK-PS for short) on large-field-of-view images that span abdomen and pelvis. Two key machine learning techniques, i.e., the knowledge-leveraged transfer fuzzy c-means (KL-TFCM) and the Laplacian support vector machine (LapSVM), are used in SCT-PK-PS. The significance of our effort is threefold: 1) Using the prior knowledge-referenced KL-TFCM clustering, SCT-PK-PS is able to group the feature data of MR images into five initial clusters of fat, soft tissue, air, bone, and bone marrow. Via these initial partitions, clusters needing to be refined are observed and for each of them a few additionally labeled examples are given as the partial supervision for the subsequent semi-supervised classification using LapSVM;2) Partial supervision is usually insufficient for conventional algorithms to learn the insightful classifier. Instead, exploiting not only the given supervision but also the manifold structure embedded primarily in numerous unlabeled data, LapSVM is capable of training multiple desired tissue-recognizers;3) Benefiting from the joint use of KL-TFCM and LapSVM, and assisted by the edge detector filter based feature extraction, the proposed SCT-PK-PS method features good recognition accuracy of tissue types, which ultimately facilitates the good transf

关键词： Abdomen intelligent transformation machine learning medical images MR pelvis synthetic CT

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Fourier Surrogate Models of Dilated Fitness Landscapes in Systems biology : or how we learned to torture optimization problems until they confess

Fourier Surrogate Models of Dilated Fitness Landscapes in Sy...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Marco S. Nobile Paolo Cazzaniga Simone Spolaor Daniela Besozzi Luca Manzoni Eindhoven University of Technology (TU/e) Eindhoven AZ The Netherlands SYSBIO/ISBE.IT Centre for Systems Biology Milano Italy University of Bergamo Bergamo Italy University of Milano-Bicocca Milano Italy Bicocca Bioinformatics Biostatistics and Bioimaging Centre (B4) Monza Italy University of Trieste Trieste Italy

ISBN: (数字)9781728194684

ISBN: (纸本)9781728194691

One of the most complex problems in Systems biology is Parameter estimation (PE), which consists in inferring the kinetic parameters of biochemical systems. The identification of an accurate parameterization, able to reproduce any observed experimental behavior, is fundamental for the definition of predictive models. PE is a non-convex, multi-modal, and non-separable problem that is usually tackled by using computational intelligence methods. When the biochemical species appear in the system in a very low amount, the intrinsic noise due to the randomness of molecular collisions cannot be neglected. In this case, stochastic simulation algorithms should be employed to properly reproduce the system dynamics. Stochastic fluctuations make the PE problem even more complicated, as they can lead to radically different values of the fitness function for the same candidate parameterization. In addition, the kinetic parameters generally follow a log-uniform distribution, so that global optima tend to be localized in the lowest orders of magnitude of the search space. To simultaneously tackle all the aforementioned issues, in this work we investigate a novel approach based on the combination of dilation functions with Fourier surrogate modeling and filtering on the fitness landscape. The results show that our approach is able to strongly simplify the PE problem for low-dimensional optimization instances.

关键词： Stochastic processes Optimization Biological system modeling Solid modeling Semantics Mathematical model computational modeling

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Spatial graphs and Convolutive Models

Spatial graphs and Convolutive Models

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Giuseppe Giacopelli Michele Migliore Domenico Tegolo Universitá degli studi di Palermo Institute of Biophysics of Palermo National Research Council Palermo Italy

ISBN: (数字)9781728194684

ISBN: (纸本)9781728194691

In the last two decades, many complex systems have benefited from the use of graph theory, and these approaches have shown robust applicability in the field of finance, computer circuits and in biological systems. Large scale models of brain systems make also a great use of random graph models. Graph theory can be instrumental in modeling the connectivity and spatial distribution of neurons, through a characterization of the relative topological properties. However, all approaches in studying brain function have been so far limited to use experimental constraints obtained at a macroscopic level (e.g. fMRI, EEG, MEG, DTI, DSI). In this contribution, we present a microscopic use (i.e. at the single neuron level) of graph theory to introduce a new model, which we call spatial convolutive model (SCM). Such a model is able to merge random graphs and Power Law models in such a way to quantitatively reproduce the topological and spatial connection distributions observed in real systems.

关键词： Erbium Brain modeling computational modeling Biological system modeling Random variables Neurons Mathematical model

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Special Issue Editorial: Intelligent Data Analysis for Sustainable Computing

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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ieee TRANSACTIONS ON SUSTAINABLE COMPUTING 2020年第3期5卷 304-307页

作者： Wu, Yulei Pan, Yi Georgalas, Nektarios Min, Geyong Univ Exeter Coll Engn Math & Phys Sci Exeter EX4 4QF Devon England Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA BT Res & Innovat British Telecom Ipswich IP5 3RE Suffolk England

The ten papers in this special section are devoted to the most recent developments and research outcomes addressing the related theoretical and practical aspects of computational intelligence solutions in sustainable computing and aims at presenting latest innovative ideas targeted at the corresponding key challenges, either from a methodological or from an application perspective.

关键词： Special Issues And Sections Sustainable Development Data Analysis Cloud Computing computational intelligence

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Deep Learning for Plant Species Classification Using Leaf Vein Morphometric

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2020年第1期17卷 82-90页

作者： Tan, Jing Wei Chang, Siow-Wee Abdul-Kareem, Sameem Yap, Hwa Jen Yong, Kien-Thai Univ Malaya Inst Biol Sci Bioinformat Programme Fac Sci Kuala Lumpur 50603 Malaysia Univ Malaya Fac Comp Sci & Informat Technol Dept Artificial Intelligence Kuala Lumpur 50603 Malaysia Univ Malaya Dept Mech Engn Fac Engn Kuala Lumpur 50603 Malaysia Univ Malaya Fac Sci Inst Biol Sci Ecol & Biodivers Programme Kuala Lumpur 50603 Malaysia

An automated plant species identification system could help botanists and layman in identifying plant species rapidly. Deep learning is robust for feature extraction as it is superior in providing deeper information of images. In this research, a new CNN-based method named D-Leaf was proposed. The leaf images were pre-processed and the features were extracted by using three different Convolutional Neural Network (CNN) models namely pre-trained AlexNet, fine-tuned AlexNet, and D-Leaf. These features were then classified by using five machine learning techniques, namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), k-Nearest-Neighbor (k-NN), Naive-Bayes (NB), and CNN. A conventional morphometric method computed the morphological measurements based on the Sobel segmented veins was employed for benchmarking purposes. The D-Leaf model achieved a comparable testing accuracy of 94.88 percent as compared to AlexNet (93.26 percent) and fine-tuned AlexNet (95.54 percent) models. In addition, CNN models performed better than the traditional morphometric measurements (66.55 percent). The features extracted from the CNN are found to be fitted well with the ANN classifier. D-Leaf can be an effective automated system for plant species identification as shown by the experimental results.

关键词： Feature extraction Veins Machine learning Image color analysis Shape Support vector machines Artificial neural networks Tropical tree deep learning convolutional network leaf vein morphometric feature extraction classification artificial neural network

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Odd Distance Anchors for Rapid Clustering

Odd Distance Anchors for Rapid Clustering

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Daniel Ashlock Haiyang Chang Matthew Stoodley University of Guelph

ISBN: (数字)9781728194684

ISBN: (纸本)9781728194691

This paper introduces a rapid clustering algorithm called anchor clustering. The algorithm was invented to permit clustering of lymphocyte antigen receptor sequences for veterinary diagnostic applications. Anchor clustering has a slow off-line and a rapid on-line phase. The off-line portion consists of locating a set of points, called anchors, by packing points into the data space so that they satisfy a minimum distance constraint. This study addresses a problem in the anchor location phase of anchor clustering. When used on discrete data, like DNA under the Hamming metric, there is a problem with data that exhibit tied minimum distances to anchors. This can be addressed by finding sets of anchors in which as many of the small distances between anchors are odd. This study tests four methods for locating sets of anchors enriched for short, odd distances. One method, which explicitly scores anchor sets on avoiding ties, works very poorly. A method that encourages odd distances is somewhat more effective, but two methods that optimize the ratio of short odd distances to short even distances achieve substantial enrichment of short odd distances. The reasons for the observed performance of different techniques are explored and possible next steps are outlined.

关键词： Clustering algorithms DNA bioinformatics Sociology Immune system Data structures Hamming distance

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Vaccinating a Population is a Programming Problem

Vaccinating a Population is a Programming Problem

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： James Alexander Hughes Michael Dubé Sheridan Houghten Daniel Ashlock Computer Science St. Francis Xavier University Antigonish Canada Computer Science Brock University St. Catharines Canada Mathematics and Statistics University of Guelph Guelph Canada

ISBN: (数字)9781728194684

ISBN: (纸本)9781728194691

It is important to understand how best to apply a limited number of vaccines to a population such that the spread of a disease, like SARS-CoV-2, is minimized. Although intuition provides a number of mitigation strategies that may be effective, they remain largely untested.A system was developed to test a given disease mitigation strategy. It is designed to work with a graph representing a real social network. A Genetic Programming system was used to discover novel mitigation strategies that are easily interpretable by a public health decision *** strategies were developed by the GP system. The strategies are easily explainable and intuitive. Novel mitigation strategies were compared to simple baseline strategies with varying success using a number of different metrics. Many of these strategies proved effective in general, however the topology of the graph influences the effectiveness of a *** system has been made publicly available and the authors call on the research community to contribute their own mitigation strategies and measure their efficacy.

关键词： Statistics Sociology Current measurement Erbium computational modeling COVID-19 Topology

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The Cross-Interpretation of QSAR Toxicological Models 16th

The Cross-Interpretation of QSAR Toxicological Models

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16th International symposium on bioinformatics Research and Applications, ISBRA 2020

作者： Tinkov, Oleg Polishchuk, Pavel Grigorev, Veniamin Porozov, Yuri Military Institute of the Ministry of Defense TiraspolTransdniestria3300 Moldova Institute of Molecular and Translational Medicine Faculty of Medicine and Dentistry Palacký University and University Hospital in Olomouc Olomouc779 00 Czech Republic Institute of Physiologically Active Compounds Russian Academy of Sciences Moscow Oblast Chernogolovka142432 Russia Moscow119991 Russia Department of Food Biotechnology and Engineering ITMO University Saint-Petersburg197101 Russia Department of Computational Biology Sirius University of Science and Technology Sochi354340 Russia

ISBN: (纸本)9783030578206

The investigation of influence of the molecular structure of different organic compounds on acute, developmental toxicity, mutagenicity has been carried out with the usage of 2D simplex representation of molecular structure and Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Machine (GBM), Partial Least Squares (PLS). Suitable QSAR (Quantitative Structure - Activity Relationships) models were obtained. The study was focused on QSAR model interpretation. The aim of the study was to develop a set of structural fragments that steadily increase various types of toxicity. The interpretation allowed to detail the molecular environment of known toxicophors and to propose new fragments. © 2020, Springer Nature Switzerland AG.

关键词： Toxicity

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An ensemble deep transfer-learning approach to identify COVID-19 cases from chest X-ray images

An ensemble deep transfer-learning approach to identify COVI...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Taki Hasan Rafi Ahsanullah University of Science and Technology Dhaka Bangladesh

ISBN: (数字)9781728194684

ISBN: (纸本)9781728194691

Novel coronavirus began in Wuhan, China back in December 2019. It has now outspread all over the world. Around 23 million people are currently affected by the novel coronavirus. It causes around 800,000 deaths globally. There are just about 300,000 people contaminated by COVID-19 in Bangladesh too. As it is an exceptional new pandemic infection, its diagnosis is challenging for the medical community. In regular cases, it is hard for developing countries to test cases frequently. The RT - PCR test is a generally utilized analysis framework for COVID-19 case detection. However, by utilizing X-ray image-based programs, recognition can diminish the expense and testing time. So it is important to program an effective recognition system to identify positive cases. In this paper, the author proposes an ensemble deep learning model, combining two state-of-art pre-trained models as ResNet-152 and DenseNet-121 to identify COVID-19 cases. The experimental validation result is immensely well with an accuracy of 98.43% on the proposed model. The author also compares the ensemble model's performance with ResNet-50 and DenseNet-121 separately.

关键词： COVID-19 X-ray imaging Deep learning Lung Computer architecture Neural networks computational modeling

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